Overview

Dataset statistics

Number of variables17
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.6 KiB
Average record size in memory142.6 B

Variable types

Text4
Numeric6
Categorical6
DateTime1

Dataset

Description경상남도 양산시 공동주택(아파트) 연한별 현황에 대한 데이터로 아파트명, 위도, 경도, 층수, 동수, 세대수, 유형, 난방방식, 승강기, 주차, 관리방법, 승인일, 준공일, 의무관리대상 등의 항목을 제공합니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15074023

Alerts

출처 has constant value ""Constant
기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도High correlation
동수 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
세대수 is highly overall correlated with 동수 and 3 other fieldsHigh correlation
승강기 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
주차 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
난방방식 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
유형 is highly imbalanced (76.8%)Imbalance
위치 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
승강기 has 38 (18.5%) zerosZeros
주차 has 8 (3.9%) zerosZeros

Reproduction

Analysis started2023-12-10 23:29:45.898471
Analysis finished2023-12-10 23:29:50.529398
Duration4.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct203
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T08:29:50.722614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length7.6097561
Min length4

Characters and Unicode

Total characters1560
Distinct characters218
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)98.0%

Sample

1st row주공2차아파트
2nd row주공3차아파트
3rd row삼전무지개아파트
4th row삼위로얄맨션아파트
5th row덕산타운아파트
ValueCountFrequency (%)
휴먼시아 3
 
1.2%
해강아파트 2
 
0.8%
일동미라주아파트 2
 
0.8%
월드메르디앙 2
 
0.8%
양산 2
 
0.8%
서창 2
 
0.8%
이지더원 2
 
0.8%
e편한세상 2
 
0.8%
협성강변아파트 1
 
0.4%
삼성임대아파트 1
 
0.4%
Other values (221) 221
92.1%
2023-12-11T08:29:51.141634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 135
 
8.7%
트 132
 
8.5%
파 127
 
8.1%
차 72
 
4.6%
대 35
 
2.2%
35
 
2.2%
산 30
 
1.9%
동 30
 
1.9%
양 30
 
1.9%
2 27
 
1.7%
Other values (208) 907
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1412
90.5%
Decimal Number 84
 
5.4%
Space Separator 35
 
2.2%
Uppercase Letter 9
 
0.6%
Open Punctuation 7
 
0.4%
Close Punctuation 7
 
0.4%
Lowercase Letter 4
 
0.3%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 135
 
9.6%
트 132
 
9.3%
파 127
 
9.0%
차 72
 
5.1%
대 35
 
2.5%
산 30
 
2.1%
동 30
 
2.1%
양 30
 
2.1%
신 24
 
1.7%
우 24
 
1.7%
Other values (191) 773
54.7%
Decimal Number
ValueCountFrequency (%)
2 27
32.1%
1 22
26.2%
3 13
15.5%
5 7
 
8.3%
4 5
 
6.0%
6 4
 
4.8%
7 3
 
3.6%
8 3
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
H 3
33.3%
L 3
33.3%
C 2
22.2%
K 1
 
11.1%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1412
90.5%
Common 135
 
8.7%
Latin 13
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 135
 
9.6%
트 132
 
9.3%
파 127
 
9.0%
차 72
 
5.1%
대 35
 
2.5%
산 30
 
2.1%
동 30
 
2.1%
양 30
 
2.1%
신 24
 
1.7%
우 24
 
1.7%
Other values (191) 773
54.7%
Common
ValueCountFrequency (%)
35
25.9%
2 27
20.0%
1 22
16.3%
3 13
 
9.6%
5 7
 
5.2%
( 7
 
5.2%
) 7
 
5.2%
4 5
 
3.7%
6 4
 
3.0%
7 3
 
2.2%
Other values (2) 5
 
3.7%
Latin
ValueCountFrequency (%)
e 4
30.8%
H 3
23.1%
L 3
23.1%
C 2
15.4%
K 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1412
90.5%
ASCII 148
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 135
 
9.6%
트 132
 
9.3%
파 127
 
9.0%
차 72
 
5.1%
대 35
 
2.5%
산 30
 
2.1%
동 30
 
2.1%
양 30
 
2.1%
신 24
 
1.7%
우 24
 
1.7%
Other values (191) 773
54.7%
ASCII
ValueCountFrequency (%)
35
23.6%
2 27
18.2%
1 22
14.9%
3 13
 
8.8%
5 7
 
4.7%
( 7
 
4.7%
) 7
 
4.7%
4 5
 
3.4%
6 4
 
2.7%
e 4
 
2.7%
Other values (7) 17
11.5%

위치
Text

UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T08:29:51.492286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length17.82439
Min length14

Characters and Unicode

Total characters3654
Distinct characters109
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)100.0%

Sample

1st row경상남도 양산시 물금읍 동중1길21
2nd row경상남도 양산시 물금읍 동중1길7
3rd row경상남도 양산시 물금읍 원동로 59
4th row경상남도 양산시 물금읍 동중7길21
5th row경상남도 양산시 물금읍 오봉로 29
ValueCountFrequency (%)
경상남도 205
22.8%
양산시 205
22.8%
물금읍 53
 
5.9%
동면 17
 
1.9%
상북면 15
 
1.7%
양주로 10
 
1.1%
하북면 10
 
1.1%
오봉로 7
 
0.8%
14 7
 
0.8%
야리로 6
 
0.7%
Other values (239) 365
40.6%
2023-12-11T08:29:51.997149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
696
19.0%
산 231
 
6.3%
상 224
 
6.1%
양 218
 
6.0%
남 213
 
5.8%
도 207
 
5.7%
경 205
 
5.6%
시 205
 
5.6%
1 147
 
4.0%
로 115
 
3.1%
Other values (99) 1193
32.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2401
65.7%
Space Separator 696
 
19.0%
Decimal Number 544
 
14.9%
Dash Punctuation 13
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 231
 
9.6%
상 224
 
9.3%
양 218
 
9.1%
남 213
 
8.9%
도 207
 
8.6%
경 205
 
8.5%
시 205
 
8.5%
로 115
 
4.8%
길 90
 
3.7%
금 70
 
2.9%
Other values (87) 623
25.9%
Decimal Number
ValueCountFrequency (%)
1 147
27.0%
3 63
11.6%
5 59
10.8%
2 55
 
10.1%
4 47
 
8.6%
7 45
 
8.3%
6 41
 
7.5%
9 31
 
5.7%
0 30
 
5.5%
8 26
 
4.8%
Space Separator
ValueCountFrequency (%)
696
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2401
65.7%
Common 1253
34.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 231
 
9.6%
상 224
 
9.3%
양 218
 
9.1%
남 213
 
8.9%
도 207
 
8.6%
경 205
 
8.5%
시 205
 
8.5%
로 115
 
4.8%
길 90
 
3.7%
금 70
 
2.9%
Other values (87) 623
25.9%
Common
ValueCountFrequency (%)
696
55.5%
1 147
 
11.7%
3 63
 
5.0%
5 59
 
4.7%
2 55
 
4.4%
4 47
 
3.8%
7 45
 
3.6%
6 41
 
3.3%
9 31
 
2.5%
0 30
 
2.4%
Other values (2) 39
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2401
65.7%
ASCII 1253
34.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
696
55.5%
1 147
 
11.7%
3 63
 
5.0%
5 59
 
4.7%
2 55
 
4.4%
4 47
 
3.8%
7 45
 
3.6%
6 41
 
3.3%
9 31
 
2.5%
0 30
 
2.4%
Other values (2) 39
 
3.1%
Hangul
ValueCountFrequency (%)
산 231
 
9.6%
상 224
 
9.3%
양 218
 
9.1%
남 213
 
8.9%
도 207
 
8.6%
경 205
 
8.5%
시 205
 
8.5%
로 115
 
4.8%
길 90
 
3.7%
금 70
 
2.9%
Other values (87) 623
25.9%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.363861
Minimum35.302472
Maximum35.495539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:52.169969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.302472
5-th percentile35.312263
Q135.329363
median35.350602
Q335.394279
95-th percentile35.422294
Maximum35.495539
Range0.193067
Interquartile range (IQR)0.064916

Descriptive statistics

Standard deviation0.044409229
Coefficient of variation (CV)0.00125578
Kurtosis0.68391895
Mean35.363861
Median Absolute Deviation (MAD)0.030843
Skewness0.9507129
Sum7249.5915
Variance0.0019721796
MonotonicityNot monotonic
2023-12-11T08:29:52.326050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.329976 1
 
0.5%
35.347635 1
 
0.5%
35.360233 1
 
0.5%
35.351441 1
 
0.5%
35.348472 1
 
0.5%
35.343464 1
 
0.5%
35.342885 1
 
0.5%
35.383569 1
 
0.5%
35.381855 1
 
0.5%
35.380897 1
 
0.5%
Other values (195) 195
95.1%
ValueCountFrequency (%)
35.302472 1
0.5%
35.304909 1
0.5%
35.306804 1
0.5%
35.309101 1
0.5%
35.310138 1
0.5%
35.310884 1
0.5%
35.310896 1
0.5%
35.311415 1
0.5%
35.311453 1
0.5%
35.311911 1
0.5%
ValueCountFrequency (%)
35.495539 1
0.5%
35.495259 1
0.5%
35.495159 1
0.5%
35.492663 1
0.5%
35.491743 1
0.5%
35.490722 1
0.5%
35.489653 1
0.5%
35.489566 1
0.5%
35.467095 1
0.5%
35.444517 1
0.5%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.06767
Minimum128.98649
Maximum129.18025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:52.502674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.98649
5-th percentile128.9946
Q1129.01871
median129.04019
Q3129.14442
95-th percentile129.16997
Maximum129.18025
Range0.193758
Interquartile range (IQR)0.125716

Descriptive statistics

Standard deviation0.062946156
Coefficient of variation (CV)0.00048769887
Kurtosis-1.2817367
Mean129.06767
Median Absolute Deviation (MAD)0.033465
Skewness0.58138078
Sum26458.872
Variance0.0039622186
MonotonicityNot monotonic
2023-12-11T08:29:52.666110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.002416 1
 
0.5%
129.021982 1
 
0.5%
129.043004 1
 
0.5%
129.035971 1
 
0.5%
129.050212 1
 
0.5%
129.026198 1
 
0.5%
129.025639 1
 
0.5%
129.028274 1
 
0.5%
129.027944 1
 
0.5%
129.027353 1
 
0.5%
Other values (195) 195
95.1%
ValueCountFrequency (%)
128.986489 1
0.5%
128.989723 1
0.5%
128.989858 1
0.5%
128.990193 1
0.5%
128.991534 1
0.5%
128.991953 1
0.5%
128.992663 1
0.5%
128.992769 1
0.5%
128.992863 1
0.5%
128.993844 1
0.5%
ValueCountFrequency (%)
129.180247 1
0.5%
129.178242 1
0.5%
129.175255 1
0.5%
129.174292 1
0.5%
129.172692 1
0.5%
129.171906 1
0.5%
129.171874 1
0.5%
129.170941 1
0.5%
129.170729 1
0.5%
129.170237 1
0.5%

층수
Text

Distinct87
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T08:29:52.863932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.2
Min length1

Characters and Unicode

Total characters656
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)27.8%

Sample

1st row5
2nd row5
3rd row15
4th row14
5th row15
ValueCountFrequency (%)
15 46
22.4%
5 20
 
9.8%
6 14
 
6.8%
17~20 5
 
2.4%
14 4
 
2.0%
20 4
 
2.0%
20~25 4
 
2.0%
21~26 3
 
1.5%
18 3
 
1.5%
14~15 3
 
1.5%
Other values (77) 99
48.3%
2023-12-11T08:29:53.257889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 151
23.0%
2 126
19.2%
5 110
16.8%
~ 96
14.6%
0 39
 
5.9%
6 32
 
4.9%
3 24
 
3.7%
8 24
 
3.7%
4 23
 
3.5%
9 17
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 560
85.4%
Math Symbol 96
 
14.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 151
27.0%
2 126
22.5%
5 110
19.6%
0 39
 
7.0%
6 32
 
5.7%
3 24
 
4.3%
8 24
 
4.3%
4 23
 
4.1%
9 17
 
3.0%
7 14
 
2.5%
Math Symbol
ValueCountFrequency (%)
~ 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 151
23.0%
2 126
19.2%
5 110
16.8%
~ 96
14.6%
0 39
 
5.9%
6 32
 
4.9%
3 24
 
3.7%
8 24
 
3.7%
4 23
 
3.5%
9 17
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 151
23.0%
2 126
19.2%
5 110
16.8%
~ 96
14.6%
0 39
 
5.9%
6 32
 
4.9%
3 24
 
3.7%
8 24
 
3.7%
4 23
 
3.5%
9 17
 
2.6%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2634146
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:53.375358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q39
95-th percentile15.8
Maximum26
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0175064
Coefficient of variation (CV)0.80108163
Kurtosis1.1776194
Mean6.2634146
Median Absolute Deviation (MAD)3
Skewness1.0870134
Sum1284
Variance25.175371
MonotonicityNot monotonic
2023-12-11T08:29:53.480178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 43
21.0%
2 23
11.2%
7 15
 
7.3%
6 15
 
7.3%
8 13
 
6.3%
4 13
 
6.3%
9 12
 
5.9%
3 12
 
5.9%
5 12
 
5.9%
13 11
 
5.4%
Other values (11) 36
17.6%
ValueCountFrequency (%)
1 43
21.0%
2 23
11.2%
3 12
 
5.9%
4 13
 
6.3%
5 12
 
5.9%
6 15
 
7.3%
7 15
 
7.3%
8 13
 
6.3%
9 12
 
5.9%
10 11
 
5.4%
ValueCountFrequency (%)
26 1
 
0.5%
24 1
 
0.5%
22 1
 
0.5%
18 4
 
2.0%
17 1
 
0.5%
16 3
 
1.5%
15 3
 
1.5%
14 3
 
1.5%
13 11
5.4%
12 3
 
1.5%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean539.77073
Minimum20
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:53.596469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile42.4
Q1148
median483
Q3796
95-th percentile1285.6
Maximum3000
Range2980
Interquartile range (IQR)648

Descriptive statistics

Standard deviation462.68761
Coefficient of variation (CV)0.85719285
Kurtosis4.2201962
Mean539.77073
Median Absolute Deviation (MAD)333
Skewness1.5395275
Sum110653
Variance214079.82
MonotonicityNot monotonic
2023-12-11T08:29:53.981513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 4
 
2.0%
72 3
 
1.5%
499 3
 
1.5%
84 3
 
1.5%
998 3
 
1.5%
160 3
 
1.5%
420 3
 
1.5%
49 2
 
1.0%
150 2
 
1.0%
30 2
 
1.0%
Other values (160) 177
86.3%
ValueCountFrequency (%)
20 1
 
0.5%
21 1
 
0.5%
29 1
 
0.5%
30 2
1.0%
34 1
 
0.5%
40 4
2.0%
42 1
 
0.5%
44 1
 
0.5%
48 2
1.0%
49 2
1.0%
ValueCountFrequency (%)
3000 1
0.5%
2280 1
0.5%
2130 1
0.5%
1768 1
0.5%
1724 1
0.5%
1663 1
0.5%
1414 1
0.5%
1385 1
0.5%
1337 1
0.5%
1300 1
0.5%

유형
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
분양
189 
임대
 
13
국민임대
 
2
국민
 
1

Length

Max length4
Median length2
Mean length2.0195122
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row분양
2nd row분양
3rd row분양
4th row분양
5th row분양

Common Values

ValueCountFrequency (%)
분양 189
92.2%
임대 13
 
6.3%
국민임대 2
 
1.0%
국민 1
 
0.5%

Length

2023-12-11T08:29:54.106241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:54.219455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분양 189
92.2%
임대 13
 
6.3%
국민임대 2
 
1.0%
국민 1
 
0.5%

난방방식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
개별
146 
지역
58 
중앙
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row개별
2nd row개별
3rd row개별
4th row개별
5th row개별

Common Values

ValueCountFrequency (%)
개별 146
71.2%
지역 58
 
28.3%
중앙 1
 
0.5%

Length

2023-12-11T08:29:54.310536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:54.391400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별 146
71.2%
지역 58
 
28.3%
중앙 1
 
0.5%

승강기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.868293
Minimum0
Maximum75
Zeros38
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:54.481975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q319
95-th percentile33
Maximum75
Range75
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.518409
Coefficient of variation (CV)0.89510001
Kurtosis3.8260809
Mean12.868293
Median Absolute Deviation (MAD)8
Skewness1.3402302
Sum2638
Variance132.67374
MonotonicityNot monotonic
2023-12-11T08:29:54.592600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 38
 
18.5%
14 9
 
4.4%
18 9
 
4.4%
17 8
 
3.9%
16 8
 
3.9%
13 8
 
3.9%
3 7
 
3.4%
2 7
 
3.4%
11 7
 
3.4%
6 7
 
3.4%
Other values (30) 97
47.3%
ValueCountFrequency (%)
0 38
18.5%
1 3
 
1.5%
2 7
 
3.4%
3 7
 
3.4%
4 7
 
3.4%
5 6
 
2.9%
6 7
 
3.4%
7 4
 
2.0%
8 5
 
2.4%
9 3
 
1.5%
ValueCountFrequency (%)
75 1
 
0.5%
55 1
 
0.5%
46 1
 
0.5%
39 1
 
0.5%
36 3
1.5%
35 2
1.0%
34 1
 
0.5%
33 2
1.0%
31 2
1.0%
30 1
 
0.5%

주차
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct181
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean509.12195
Minimum0
Maximum2626
Zeros8
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T08:29:54.702804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q181
median400
Q3777
95-th percentile1439.2
Maximum2626
Range2626
Interquartile range (IQR)696

Descriptive statistics

Standard deviation483.59043
Coefficient of variation (CV)0.94985186
Kurtosis1.5974525
Mean509.12195
Median Absolute Deviation (MAD)330
Skewness1.1805009
Sum104370
Variance233859.71
MonotonicityNot monotonic
2023-12-11T08:29:54.824923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
3.9%
70 5
 
2.4%
60 3
 
1.5%
410 2
 
1.0%
110 2
 
1.0%
15 2
 
1.0%
25 2
 
1.0%
81 2
 
1.0%
100 2
 
1.0%
20 2
 
1.0%
Other values (171) 175
85.4%
ValueCountFrequency (%)
0 8
3.9%
10 1
 
0.5%
13 1
 
0.5%
15 2
 
1.0%
18 1
 
0.5%
20 2
 
1.0%
21 1
 
0.5%
23 1
 
0.5%
25 2
 
1.0%
29 1
 
0.5%
ValueCountFrequency (%)
2626 1
0.5%
2184 1
0.5%
1840 1
0.5%
1760 1
0.5%
1681 1
0.5%
1650 1
0.5%
1552 1
0.5%
1534 1
0.5%
1494 1
0.5%
1493 1
0.5%

관리방법
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
위탁
102 
자치
101 
-
 
2

Length

Max length2
Median length2
Mean length1.9902439
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자치
2nd row자치
3rd row위탁
4th row자치
5th row자치

Common Values

ValueCountFrequency (%)
위탁 102
49.8%
자치 101
49.3%
- 2
 
1.0%

Length

2023-12-11T08:29:54.931739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:55.020937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁 102
49.8%
자치 101
49.3%
2
 
1.0%

승인일
Text

Distinct183
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T08:29:55.254268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.9609756
Min length1

Characters and Unicode

Total characters2042
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique166 ?
Unique (%)81.0%

Sample

1st row1990-03-20
2nd row1990-05-27
3rd row1992-10-24
4th row1991-08-10
5th row1991-10-31
ValueCountFrequency (%)
2004-12-30 4
 
2.0%
2013-11-07 3
 
1.5%
2012-06-21 3
 
1.5%
2005-12-13 3
 
1.5%
2006-11-30 2
 
1.0%
1991-02-13 2
 
1.0%
2000-08-12 2
 
1.0%
2016-02-12 2
 
1.0%
2015-02-10 2
 
1.0%
1990-07-07 2
 
1.0%
Other values (173) 180
87.8%
2023-12-11T08:29:55.649506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 408
20.0%
0 404
19.8%
1 369
18.1%
2 253
12.4%
9 240
11.8%
3 74
 
3.6%
4 69
 
3.4%
8 69
 
3.4%
7 54
 
2.6%
6 53
 
2.6%
Other values (2) 49
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1633
80.0%
Dash Punctuation 408
 
20.0%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 404
24.7%
1 369
22.6%
2 253
15.5%
9 240
14.7%
3 74
 
4.5%
4 69
 
4.2%
8 69
 
4.2%
7 54
 
3.3%
6 53
 
3.2%
5 48
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 408
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2042
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 408
20.0%
0 404
19.8%
1 369
18.1%
2 253
12.4%
9 240
11.8%
3 74
 
3.6%
4 69
 
3.4%
8 69
 
3.4%
7 54
 
2.6%
6 53
 
2.6%
Other values (2) 49
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 408
20.0%
0 404
19.8%
1 369
18.1%
2 253
12.4%
9 240
11.8%
3 74
 
3.6%
4 69
 
3.4%
8 69
 
3.4%
7 54
 
2.6%
6 53
 
2.6%
Other values (2) 49
 
2.4%

준공일
Date

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum1981-12-22 00:00:00
Maximum2020-01-03 00:00:00
2023-12-11T08:29:55.881348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:56.011883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

의무관리대상
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
의무
151 
<NA>
52 
비의무
 
2

Length

Max length4
Median length2
Mean length2.5170732
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row의무
2nd row의무
3rd row의무
4th row<NA>
5th row의무

Common Values

ValueCountFrequency (%)
의무 151
73.7%
<NA> 52
 
25.4%
비의무 2
 
1.0%

Length

2023-12-11T08:29:56.144270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:56.251901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무 151
73.7%
na 52
 
25.4%
비의무 2
 
1.0%

출처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
기본현황
205 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기본현황
2nd row기본현황
3rd row기본현황
4th row기본현황
5th row기본현황

Common Values

ValueCountFrequency (%)
기본현황 205
100.0%

Length

2023-12-11T08:29:56.346373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:56.431410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본현황 205
100.0%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2020-11-30
205 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-11-30
2nd row2020-11-30
3rd row2020-11-30
4th row2020-11-30
5th row2020-11-30

Common Values

ValueCountFrequency (%)
2020-11-30 205
100.0%

Length

2023-12-11T08:29:56.531537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:29:56.637251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-11-30 205
100.0%

Interactions

2023-12-11T08:29:49.664423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:46.475210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:46.940608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.629976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:48.474153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.058468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.766127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:46.552678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.018028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.717778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:48.587159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.158774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.859263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:46.628121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.318783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.813144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:48.677427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.262918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.934907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:46.709236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.392212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.942136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:48.763836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.350670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:50.033966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:46.796277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.469592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:48.086814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:48.859227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.439808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:50.129270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:46.874651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:47.553629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:48.318479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:48.966683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:29:49.569084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:29:56.718213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도층수동수세대수유형난방방식승강기주차관리방법의무관리대상
위도1.0000.8420.0000.2960.3130.0000.6640.2310.3770.5030.271
경도0.8421.0000.0000.4290.2640.0000.7640.3430.2730.6190.061
층수0.0000.0001.0000.8940.7950.7550.4160.8420.9350.9181.000
동수0.2960.4290.8941.0000.8130.0000.7080.9490.8330.7500.000
세대수0.3130.2640.7950.8131.0000.2970.7160.8560.8750.6020.142
유형0.0000.0000.7550.0000.2971.0000.0000.0000.1770.1440.000
난방방식0.6640.7640.4160.7080.7160.0001.0000.6020.5990.7680.000
승강기0.2310.3430.8420.9490.8560.0000.6021.0000.8480.6930.100
주차0.3770.2730.9350.8330.8750.1770.5990.8481.0000.6520.000
관리방법0.5030.6190.9180.7500.6020.1440.7680.6930.6521.0000.304
의무관리대상0.2710.0611.0000.0000.1420.0000.0000.1000.0000.3041.000
2023-12-11T08:29:56.836799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형관리방법난방방식의무관리대상
유형1.0000.1360.0000.000
관리방법0.1361.0000.4270.488
난방방식0.0000.4271.0000.000
의무관리대상0.0000.4880.0001.000
2023-12-11T08:29:56.934870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도동수세대수승강기주차유형난방방식관리방법의무관리대상
위도1.0000.855-0.388-0.363-0.272-0.4240.0000.5050.3430.264
경도0.8551.000-0.276-0.270-0.165-0.3350.0000.4550.3370.046
동수-0.388-0.2761.0000.9260.8590.9170.1140.4460.4480.000
세대수-0.363-0.2700.9261.0000.9290.9560.1340.5990.4650.103
승강기-0.272-0.1650.8590.9291.0000.8940.0000.3140.3990.096
주차-0.424-0.3350.9170.9560.8941.0000.1040.4350.4920.000
유형0.0000.0000.1140.1340.0000.1041.0000.0000.1360.000
난방방식0.5050.4550.4460.5990.3140.4350.0001.0000.4270.000
관리방법0.3430.3370.4480.4650.3990.4920.1360.4271.0000.488
의무관리대상0.2640.0460.0000.1030.0960.0000.0000.0000.4881.000

Missing values

2023-12-11T08:29:50.250213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:29:50.459158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

아파트명위치위도경도층수동수세대수유형난방방식승강기주차관리방법승인일준공일의무관리대상출처기준일자
0주공2차아파트경상남도 양산시 물금읍 동중1길2135.329976129.00241659420분양개별0104자치1990-03-201990-03-20의무기본현황2020-11-30
1주공3차아파트경상남도 양산시 물금읍 동중1길735.328111129.002984511410분양개별0226자치1990-05-271990-05-27의무기본현황2020-11-30
2삼전무지개아파트경상남도 양산시 물금읍 원동로 5935.313073128.986489152168분양개별441위탁1992-10-241992-10-24의무기본현황2020-11-30
3삼위로얄맨션아파트경상남도 양산시 물금읍 동중7길2135.331941129.00535614188분양개별369자치1991-08-101992-11-20<NA>기본현황2020-11-30
4덕산타운아파트경상남도 양산시 물금읍 오봉로 2935.327165128.996963156483분양개별17351자치1991-10-311993-08-02의무기본현황2020-11-30
5황전아파트경상남도 양산시 물금읍 오봉로 1535.326234128.998326155496분양개별17371자치1991-10-311994-06-03의무기본현황2020-11-30
6경민아파트경상남도 양산시 물금읍 황산로 70735.332837129.008661153210분양개별7163자치1993-03-051994-10-31의무기본현황2020-11-30
7대동타운아파트경상남도 양산시 물금읍 오봉로 16535.335363129.0067511591122분양개별25997위탁1993-07-211995-09-26의무기본현황2020-11-30
8범어그린피아아파트경상남도 양산시 물금읍 오봉로 18535.334585129.00920565300분양개별0149자치1992-08-291992-08-29의무기본현황2020-11-30
9현대아파트경상남도 양산시 물금읍 오봉로 18035.333398129.006778158956분양개별25552위탁1993-09-091996-03-20의무기본현황2020-11-30
아파트명위치위도경도층수동수세대수유형난방방식승강기주차관리방법승인일준공일의무관리대상출처기준일자
195KCC 스위첸경상남도 양산시 평산회야로 16735.378576129.15315528~386553분양개별13686자치2016-01-292019-10-18의무기본현황2020-11-30
196경보1차아파트경상남도 양산시 덕계회야길 735.370165129.143948152227분양개별8171자치1992-03-261992-03-26의무기본현황2020-11-30
197대승1차아파트경상남도 양산시 웅상대로 86635.376129129.157245246790분양개별17413위탁1991-08-101993-12-02의무기본현황2020-11-30
198대승2차아파트경상남도 양산시 덕계북길 935.384674129.157073264476분양개별11330위탁1992-05-021994-11-14의무기본현황2020-11-30
199웅상 쇼핑타운경상남도 양산시 덕계로 10535.376865129.153266151121분양개별262자치1994-03-171997-02-04<NA>기본현황2020-11-30
200부영벽산아파트경상남도 양산시 덕계11길 1235.376569129.15198422~256863분양개별18563위탁1999-02-011999-02-01의무기본현황2020-11-30
201동일스위트2차경상남도 양산시 덕계5길 1435.373344129.14782518~257790분양개별17785위탁2000-05-252003-02-08의무기본현황2020-11-30
202경동 스마트홈경상남도 양산시 신덕계로 3435.377988129.15841317~207487분양개별12490위탁2011-06-202014-01-16의무기본현황2020-11-30
203우성스마트뷰경상남도 양산시 덕계회야길 1635.371873129.14654529~348604분양개별18699자치2016-08-122019-10-22의무기본현황2020-11-30
204두산 위브 1차경상남도 양산시 신덕계3길 3635.372561129.16160425~29131337분양개별291494자치2015-09-082019-11-28의무기본현황2020-11-30